In order to manage sensors in a heterogeneous, multi-sensor environment, it is necessary to associate each information request or sensory input with a value representing the significance of that information request or sensory input. Conventionally, the amount of information collected using sensors was maximized in order to decrease the uncertainty between a mathematical model of the world and the world itself. However, while information is useful in establishing a reliable mathematical model of the world and is therefore a necessary condition for sensor management, the collection of thorough sensory information is not a sufficient condition for a comprehensive approach to sensor management. Rather, without exercising some form of a discretion with regard to information sought and/or collected, computations performed based on the collected information may produce skewed results and may be unnecessarily complex and resource consuming.
The same principles and problems apply to conventional decision-making models. Conventional decision making modes include abstract goals and discrete action tasks, but do not provide a mechanism for quantitatively relating the two. For that reason, it is difficult to determine which of several tasks has the highest relative contribution to the accomplishment of one or more system goals, and thus, which task is most critical to the satisfaction of the system goals.